Overview

Brought to you by YData

Dataset statistics

Number of variables13
Number of observations16.978.680
Missing cells0
Missing cells (%)0.0%
Duplicate rows171.936
Duplicate rows (%)1.0%
Total size in memory5.4 GiB
Average record size in memory343.9 B

Variable types

Numeric7
Categorical5
Text1

Alerts

Dataset has 171936 (1.0%) duplicate rowsDuplicates
brand is highly overall correlated with cat1 and 1 other fieldsHigh correlation
cat1 is highly overall correlated with brand and 1 other fieldsHigh correlation
cat2 is highly overall correlated with brand and 1 other fieldsHigh correlation
cust_request_qty is highly overall correlated with cust_request_tn and 1 other fieldsHigh correlation
cust_request_tn is highly overall correlated with cust_request_qty and 1 other fieldsHigh correlation
product_id is highly overall correlated with sku_sizeHigh correlation
sku_size is highly overall correlated with product_idHigh correlation
tn is highly overall correlated with cust_request_qty and 1 other fieldsHigh correlation
plan_precios_cuidados is highly imbalanced (98.2%) Imbalance
cust_request_tn is highly skewed (γ1 = 95.58229409) Skewed
tn is highly skewed (γ1 = 96.08283194) Skewed
periodo is uniformly distributed Uniform
cust_request_qty has 14646469 (86.3%) zeros Zeros
cust_request_tn has 14646469 (86.3%) zeros Zeros
tn has 14646469 (86.3%) zeros Zeros
stock_final has 10719135 (63.1%) zeros Zeros

Reproduction

Analysis started2025-06-03 23:20:12.938511
Analysis finished2025-06-03 23:34:36.897129
Duration14 minutes and 23.96 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

customer_id
Real number (ℝ)

Distinct597
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10303.682
Minimum10001
Maximum10637
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size129.5 MiB
2025-06-03T20:34:37.156055image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum10001
5-th percentile10030
Q110150
median10299
Q310455
95-th percentile10590
Maximum10637
Range636
Interquartile range (IQR)305

Descriptive statistics

Standard deviation178.7095
Coefficient of variation (CV)0.017344237
Kurtosis-1.1520989
Mean10303.682
Median Absolute Deviation (MAD)153
Skewness0.073405409
Sum1.7494292 × 1011
Variance31937.087
MonotonicityIncreasing
2025-06-03T20:34:37.475871image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10001 28440
 
0.2%
10395 28440
 
0.2%
10398 28440
 
0.2%
10399 28440
 
0.2%
10400 28440
 
0.2%
10402 28440
 
0.2%
10404 28440
 
0.2%
10405 28440
 
0.2%
10406 28440
 
0.2%
10407 28440
 
0.2%
Other values (587) 16694280
98.3%
ValueCountFrequency (%)
10001 28440
0.2%
10002 28440
0.2%
10003 28440
0.2%
10004 28440
0.2%
10005 28440
0.2%
10006 28440
0.2%
10007 28440
0.2%
10008 28440
0.2%
10009 28440
0.2%
10010 28440
0.2%
ValueCountFrequency (%)
10637 28440
0.2%
10636 28440
0.2%
10635 28440
0.2%
10634 28440
0.2%
10633 28440
0.2%
10632 28440
0.2%
10631 28440
0.2%
10630 28440
0.2%
10629 28440
0.2%
10626 28440
0.2%

product_id
Real number (ℝ)

High correlation 

Distinct780
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20536.509
Minimum20001
Maximum21276
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size129.5 MiB
2025-06-03T20:34:37.801482image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum20001
5-th percentile20037
Q120233
median20504
Q320815
95-th percentile21167
Maximum21276
Range1275
Interquartile range (IQR)582

Descriptive statistics

Standard deviation354.77362
Coefficient of variation (CV)0.017275264
Kurtosis-1.0199484
Mean20536.509
Median Absolute Deviation (MAD)285
Skewness0.30801862
Sum3.4868281 × 1011
Variance125864.32
MonotonicityNot monotonic
2025-06-03T20:34:38.171690image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20278 42984
 
0.3%
20192 42984
 
0.3%
20020 42984
 
0.3%
20021 42984
 
0.3%
20022 42984
 
0.3%
20010 42984
 
0.3%
20100 42984
 
0.3%
20230 42984
 
0.3%
20623 42984
 
0.3%
20037 42984
 
0.3%
Other values (770) 16548840
97.5%
ValueCountFrequency (%)
20001 21492
0.1%
20002 21492
0.1%
20003 21492
0.1%
20004 21492
0.1%
20005 21492
0.1%
20006 21492
0.1%
20007 21492
0.1%
20008 21492
0.1%
20009 21492
0.1%
20010 42984
0.3%
ValueCountFrequency (%)
21276 21492
0.1%
21267 21492
0.1%
21266 21492
0.1%
21265 21492
0.1%
21263 21492
0.1%
21262 21492
0.1%
21259 21492
0.1%
21256 21492
0.1%
21252 21492
0.1%
21248 21492
0.1%

periodo
Categorical

Uniform 

Distinct36
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size955.3 MiB
2017-01-01
 
471630
2017-02-01
 
471630
2017-09-01
 
471630
2017-03-01
 
471630
2017-04-01
 
471630
Other values (31)
14620530 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters169.786.800
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017-01-01
2nd row2017-02-01
3rd row2017-03-01
4th row2017-04-01
5th row2017-05-01

Common Values

ValueCountFrequency (%)
2017-01-01 471630
 
2.8%
2017-02-01 471630
 
2.8%
2017-09-01 471630
 
2.8%
2017-03-01 471630
 
2.8%
2017-04-01 471630
 
2.8%
2017-05-01 471630
 
2.8%
2017-06-01 471630
 
2.8%
2017-07-01 471630
 
2.8%
2017-08-01 471630
 
2.8%
2017-10-01 471630
 
2.8%
Other values (26) 12262380
72.2%

Length

2025-06-03T20:34:38.466776image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2017-01-01 471630
 
2.8%
2017-02-01 471630
 
2.8%
2019-03-01 471630
 
2.8%
2018-09-01 471630
 
2.8%
2018-10-01 471630
 
2.8%
2018-11-01 471630
 
2.8%
2018-12-01 471630
 
2.8%
2019-01-01 471630
 
2.8%
2019-02-01 471630
 
2.8%
2019-04-01 471630
 
2.8%
Other values (26) 12262380
72.2%

Most occurring characters

ValueCountFrequency (%)
0 48106260
28.3%
1 41031810
24.2%
- 33957360
20.0%
2 19808460
11.7%
7 7074450
 
4.2%
9 7074450
 
4.2%
8 7074450
 
4.2%
3 1414890
 
0.8%
4 1414890
 
0.8%
5 1414890
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 135829440
80.0%
Dash Punctuation 33957360
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 48106260
35.4%
1 41031810
30.2%
2 19808460
14.6%
7 7074450
 
5.2%
9 7074450
 
5.2%
8 7074450
 
5.2%
3 1414890
 
1.0%
4 1414890
 
1.0%
5 1414890
 
1.0%
6 1414890
 
1.0%
Dash Punctuation
ValueCountFrequency (%)
- 33957360
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 169786800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 48106260
28.3%
1 41031810
24.2%
- 33957360
20.0%
2 19808460
11.7%
7 7074450
 
4.2%
9 7074450
 
4.2%
8 7074450
 
4.2%
3 1414890
 
0.8%
4 1414890
 
0.8%
5 1414890
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 169786800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 48106260
28.3%
1 41031810
24.2%
- 33957360
20.0%
2 19808460
11.7%
7 7074450
 
4.2%
9 7074450
 
4.2%
8 7074450
 
4.2%
3 1414890
 
0.8%
4 1414890
 
0.8%
5 1414890
 
0.8%

plan_precios_cuidados
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size842.0 MiB
0.0
16950750 
1.0
 
27930

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters50.936.040
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 16950750
99.8%
1.0 27930
 
0.2%

Length

2025-06-03T20:34:38.678743image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T20:34:38.883292image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.0 16950750
99.8%
1.0 27930
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 33929430
66.6%
. 16978680
33.3%
1 27930
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 33957360
66.7%
Other Punctuation 16978680
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 33929430
99.9%
1 27930
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 16978680
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 50936040
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 33929430
66.6%
. 16978680
33.3%
1 27930
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 50936040
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 33929430
66.6%
. 16978680
33.3%
1 27930
 
0.1%

cust_request_qty
Real number (ℝ)

High correlation  Zeros 

Distinct85
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.29828544
Minimum0
Maximum92
Zeros14646469
Zeros (%)86.3%
Negative0
Negative (%)0.0%
Memory size129.5 MiB
2025-06-03T20:34:39.116995image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum92
Range92
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.5508814
Coefficient of variation (CV)5.1993198
Kurtosis302.13614
Mean0.29828544
Median Absolute Deviation (MAD)0
Skewness14.486984
Sum5064493
Variance2.4052331
MonotonicityNot monotonic
2025-06-03T20:34:39.370018image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 14646469
86.3%
1 1607873
 
9.5%
2 355885
 
2.1%
3 119199
 
0.7%
4 64614
 
0.4%
5 36682
 
0.2%
6 24818
 
0.1%
7 18335
 
0.1%
8 14547
 
0.1%
9 11091
 
0.1%
Other values (75) 79167
 
0.5%
ValueCountFrequency (%)
0 14646469
86.3%
1 1607873
 
9.5%
2 355885
 
2.1%
3 119199
 
0.7%
4 64614
 
0.4%
5 36682
 
0.2%
6 24818
 
0.1%
7 18335
 
0.1%
8 14547
 
0.1%
9 11091
 
0.1%
ValueCountFrequency (%)
92 1
< 0.1%
90 1
< 0.1%
88 1
< 0.1%
85 2
< 0.1%
84 1
< 0.1%
83 1
< 0.1%
79 1
< 0.1%
78 1
< 0.1%
77 1
< 0.1%
76 1
< 0.1%

cust_request_tn
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct92002
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.071656524
Minimum0
Maximum551.56137
Zeros14646469
Zeros (%)86.3%
Negative0
Negative (%)0.0%
Memory size129.5 MiB
2025-06-03T20:34:39.615424image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.086
Maximum551.56137
Range551.56137
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.3397632
Coefficient of variation (CV)18.697016
Kurtosis17552.36
Mean0.071656524
Median Absolute Deviation (MAD)0
Skewness95.582294
Sum1216633.2
Variance1.7949654
MonotonicityNot monotonic
2025-06-03T20:34:39.870108image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 14646469
86.3%
0.01638 14120
 
0.1%
0.00218 12909
 
0.1%
0.00983 11954
 
0.1%
0.01092 10709
 
0.1%
0.00546 10651
 
0.1%
0.04095 10583
 
0.1%
0.03276 10390
 
0.1%
0.00109 10240
 
0.1%
0.00491 10189
 
0.1%
Other values (91992) 2230466
 
13.1%
ValueCountFrequency (%)
0 14646469
86.3%
0.0001 170
 
< 0.1%
0.00013 79
 
< 0.1%
0.00018 159
 
< 0.1%
0.0002 238
 
< 0.1%
0.00021 628
 
< 0.1%
0.00023 744
 
< 0.1%
0.00025 299
 
< 0.1%
0.00026 217
 
< 0.1%
0.00029 137
 
< 0.1%
ValueCountFrequency (%)
551.56137 1
< 0.1%
510.65893 1
< 0.1%
444.41192 1
< 0.1%
439.90647 1
< 0.1%
437.37767 1
< 0.1%
416.64823 1
< 0.1%
407.02225 1
< 0.1%
393.26092 1
< 0.1%
389.02653 1
< 0.1%
384.82574 1
< 0.1%

tn
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct91943
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.070047949
Minimum0
Maximum547.87849
Zeros14646469
Zeros (%)86.3%
Negative0
Negative (%)0.0%
Memory size129.5 MiB
2025-06-03T20:34:40.136731image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.0859
Maximum547.87849
Range547.87849
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.2914311
Coefficient of variation (CV)18.436387
Kurtosis17961.662
Mean0.070047949
Median Absolute Deviation (MAD)0
Skewness96.082832
Sum1189321.7
Variance1.6677942
MonotonicityNot monotonic
2025-06-03T20:34:40.399144image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 14646469
86.3%
0.01638 14125
 
0.1%
0.00218 12908
 
0.1%
0.00983 11954
 
0.1%
0.01092 10714
 
0.1%
0.00546 10653
 
0.1%
0.04095 10583
 
0.1%
0.03276 10404
 
0.1%
0.00109 10242
 
0.1%
0.00491 10188
 
0.1%
Other values (91933) 2230440
 
13.1%
ValueCountFrequency (%)
0 14646469
86.3%
0.0001 170
 
< 0.1%
0.00013 79
 
< 0.1%
0.00018 159
 
< 0.1%
0.0002 238
 
< 0.1%
0.00021 628
 
< 0.1%
0.00023 746
 
< 0.1%
0.00025 299
 
< 0.1%
0.00026 217
 
< 0.1%
0.00029 137
 
< 0.1%
ValueCountFrequency (%)
547.87849 1
< 0.1%
469.45761 1
< 0.1%
439.90647 1
< 0.1%
437.37767 1
< 0.1%
430.90803 1
< 0.1%
414.05146 1
< 0.1%
389.02653 1
< 0.1%
386.60688 1
< 0.1%
384.82574 1
< 0.1%
379.4427 1
< 0.1%

stock_final
Real number (ℝ)

Zeros 

Distinct10047
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.3507683
Minimum-13.66656
Maximum1562.0245
Zeros10719135
Zeros (%)63.1%
Negative159399
Negative (%)0.9%
Memory size129.5 MiB
2025-06-03T20:34:40.651658image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-13.66656
5-th percentile0
Q10
median0
Q32.828945
95-th percentile38.085946
Maximum1562.0245
Range1575.691
Interquartile range (IQR)2.828945

Descriptive statistics

Standard deviation39.37932
Coefficient of variation (CV)4.7156523
Kurtosis373.86696
Mean8.3507683
Median Absolute Deviation (MAD)0
Skewness15.862276
Sum1.4178502 × 108
Variance1550.7309
MonotonicityNot monotonic
2025-06-03T20:34:41.274812image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 10719135
63.1%
0.00109 2388
 
< 0.1%
-0.01747 2388
 
< 0.1%
0.04423 2388
 
< 0.1%
36.58637 2388
 
< 0.1%
3.42342 2388
 
< 0.1%
0.0819 2388
 
< 0.1%
0.7204 2388
 
< 0.1%
0.049 2388
 
< 0.1%
0.02092 2388
 
< 0.1%
Other values (10037) 6238053
36.7%
ValueCountFrequency (%)
-13.66656 597
< 0.1%
-13.33127 597
< 0.1%
-8.19961 597
< 0.1%
-8.15986 597
< 0.1%
-7.7212 597
< 0.1%
-5.86579 597
< 0.1%
-5.28091 597
< 0.1%
-5.0992 597
< 0.1%
-4.87775 597
< 0.1%
-4.44673 597
< 0.1%
ValueCountFrequency (%)
1562.02448 597
< 0.1%
1284.38214 597
< 0.1%
1212.36734 597
< 0.1%
1146.09799 597
< 0.1%
1097.55623 597
< 0.1%
1057.38804 597
< 0.1%
1037.85386 597
< 0.1%
1031.01561 597
< 0.1%
978.16446 597
< 0.1%
916.3419 597
< 0.1%

cat1
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size835.0 MiB
PC
9585432 
HC
4104972 
FOODS
3159324 
REF
 
128952

Length

Max length5
Median length2
Mean length2.5658228
Min length2

Characters and Unicode

Total characters43.564.284
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHC
2nd rowHC
3rd rowHC
4th rowHC
5th rowHC

Common Values

ValueCountFrequency (%)
PC 9585432
56.5%
HC 4104972
24.2%
FOODS 3159324
 
18.6%
REF 128952
 
0.8%

Length

2025-06-03T20:34:41.532386image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T20:34:41.744451image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
pc 9585432
56.5%
hc 4104972
24.2%
foods 3159324
 
18.6%
ref 128952
 
0.8%

Most occurring characters

ValueCountFrequency (%)
C 13690404
31.4%
P 9585432
22.0%
O 6318648
14.5%
H 4104972
 
9.4%
F 3288276
 
7.5%
D 3159324
 
7.3%
S 3159324
 
7.3%
R 128952
 
0.3%
E 128952
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 43564284
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 13690404
31.4%
P 9585432
22.0%
O 6318648
14.5%
H 4104972
 
9.4%
F 3288276
 
7.5%
D 3159324
 
7.3%
S 3159324
 
7.3%
R 128952
 
0.3%
E 128952
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 43564284
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 13690404
31.4%
P 9585432
22.0%
O 6318648
14.5%
H 4104972
 
9.4%
F 3288276
 
7.5%
D 3159324
 
7.3%
S 3159324
 
7.3%
R 128952
 
0.3%
E 128952
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 43564284
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 13690404
31.4%
P 9585432
22.0%
O 6318648
14.5%
H 4104972
 
9.4%
F 3288276
 
7.5%
D 3159324
 
7.3%
S 3159324
 
7.3%
R 128952
 
0.3%
E 128952
 
0.3%

cat2
Categorical

High correlation 

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size916.0 MiB
CABELLO
4405860 
DEOS
2643516 
SOPAS Y CALDOS
1891296 
HOGAR
1353996 
ROPA LAVADO
1225044 
Other values (10)
5458968 

Length

Max length19
Median length14
Mean length7.5708861
Min length2

Characters and Unicode

Total characters128.543.652
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowROPA LAVADO
2nd rowROPA LAVADO
3rd rowROPA LAVADO
4th rowROPA LAVADO
5th rowROPA LAVADO

Common Values

ValueCountFrequency (%)
CABELLO 4405860
25.9%
DEOS 2643516
15.6%
SOPAS Y CALDOS 1891296
11.1%
HOGAR 1353996
 
8.0%
ROPA LAVADO 1225044
 
7.2%
PIEL2 1225044
 
7.2%
ADEREZOS 1074600
 
6.3%
PIEL1 1010124
 
5.9%
VAJILLA 709236
 
4.2%
ROPA ACONDICIONADOR 451332
 
2.7%
Other values (5) 988632
 
5.8%

Length

2025-06-03T20:34:41.989669image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
cabello 4405860
19.6%
deos 2643516
11.8%
sopas 1891296
8.4%
y 1891296
8.4%
caldos 1891296
8.4%
ropa 1719360
 
7.6%
hogar 1353996
 
6.0%
lavado 1225044
 
5.4%
piel2 1225044
 
5.4%
aderezos 1074600
 
4.8%
Other values (8) 3159324
14.1%

Most occurring characters

ValueCountFrequency (%)
O 18590580
14.5%
A 17816868
13.9%
L 16204968
12.6%
E 12185964
9.5%
S 9950796
7.7%
D 8038008
 
6.3%
C 7242804
 
5.6%
P 6168204
 
4.8%
5501952
 
4.3%
R 5115096
 
4.0%
Other values (14) 21728412
16.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 120806532
94.0%
Space Separator 5501952
 
4.3%
Decimal Number 2235168
 
1.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 18590580
15.4%
A 17816868
14.7%
L 16204968
13.4%
E 12185964
10.1%
S 9950796
8.2%
D 8038008
6.7%
C 7242804
 
6.0%
P 6168204
 
5.1%
R 5115096
 
4.2%
B 4405860
 
3.6%
Other values (11) 15087384
12.5%
Decimal Number
ValueCountFrequency (%)
2 1225044
54.8%
1 1010124
45.2%
Space Separator
ValueCountFrequency (%)
5501952
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 120806532
94.0%
Common 7737120
 
6.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 18590580
15.4%
A 17816868
14.7%
L 16204968
13.4%
E 12185964
10.1%
S 9950796
8.2%
D 8038008
6.7%
C 7242804
 
6.0%
P 6168204
 
5.1%
R 5115096
 
4.2%
B 4405860
 
3.6%
Other values (11) 15087384
12.5%
Common
ValueCountFrequency (%)
5501952
71.1%
2 1225044
 
15.8%
1 1010124
 
13.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 128543652
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O 18590580
14.5%
A 17816868
13.9%
L 16204968
12.6%
E 12185964
9.5%
S 9950796
7.7%
D 8038008
 
6.3%
C 7242804
 
5.6%
P 6168204
 
4.8%
5501952
 
4.3%
R 5115096
 
4.0%
Other values (14) 21728412
16.9%

cat3
Text

Distinct84
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size930.3 MiB
2025-06-03T20:34:42.357471image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length19
Median length16
Mean length7.9481013
Min length3

Characters and Unicode

Total characters134.948.268
Distinct characters51
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLiquido
2nd rowLiquido
3rd rowLiquido
4th rowLiquido
5th rowLiquido
ValueCountFrequency (%)
shampoo 1998756
 
10.0%
aero 1676376
 
8.4%
acondicionador 1654884
 
8.3%
jabon 816696
 
4.1%
liquido 730728
 
3.6%
sopas 687744
 
3.4%
polvo 623268
 
3.1%
mayonesa 558792
 
2.8%
noaero 494316
 
2.5%
gel 472824
 
2.4%
Other values (80) 10316160
51.5%
2025-06-03T20:34:42.974228image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 11820600
 
8.8%
O 10187208
 
7.5%
a 9628416
 
7.1%
A 9370512
 
6.9%
e 6769980
 
5.0%
C 6189696
 
4.6%
r 5931792
 
4.4%
S 4577796
 
3.4%
i 4470336
 
3.3%
I 4448844
 
3.3%
Other values (41) 61553088
45.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 68473512
50.7%
Uppercase Letter 63422892
47.0%
Space Separator 3051864
 
2.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 11820600
17.3%
a 9628416
14.1%
e 6769980
9.9%
r 5931792
8.7%
i 4470336
 
6.5%
l 4384368
 
6.4%
s 3825576
 
5.6%
n 3782592
 
5.5%
u 2557548
 
3.7%
d 2428596
 
3.5%
Other values (15) 12873708
18.8%
Uppercase Letter
ValueCountFrequency (%)
O 10187208
16.1%
A 9370512
14.8%
C 6189696
9.8%
S 4577796
7.2%
I 4448844
7.0%
N 4233924
6.7%
D 3976020
 
6.3%
P 3589164
 
5.7%
M 3567672
 
5.6%
R 2879928
 
4.5%
Other values (15) 10402128
16.4%
Space Separator
ValueCountFrequency (%)
3051864
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 131896404
97.7%
Common 3051864
 
2.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 11820600
 
9.0%
O 10187208
 
7.7%
a 9628416
 
7.3%
A 9370512
 
7.1%
e 6769980
 
5.1%
C 6189696
 
4.7%
r 5931792
 
4.5%
S 4577796
 
3.5%
i 4470336
 
3.4%
I 4448844
 
3.4%
Other values (40) 58501224
44.4%
Common
ValueCountFrequency (%)
3051864
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 134561412
99.7%
None 386856
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 11820600
 
8.8%
O 10187208
 
7.6%
a 9628416
 
7.2%
A 9370512
 
7.0%
e 6769980
 
5.0%
C 6189696
 
4.6%
r 5931792
 
4.4%
S 4577796
 
3.4%
i 4470336
 
3.3%
I 4448844
 
3.3%
Other values (40) 61166232
45.5%
None
ValueCountFrequency (%)
ñ 386856
100.0%

brand
Categorical

High correlation 

Distinct35
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size895.6 MiB
NIVEA
2342628 
SHAMPOO3
1805328 
MAGGI
1740852 
DEOS1
1676376 
MUSCULO
1396980 
Other values (30)
8016516 

Length

Max length9
Median length8
Mean length6.3088608
Min length3

Characters and Unicode

Total characters107.116.128
Distinct characters35
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowARIEL
2nd rowARIEL
3rd rowARIEL
4th rowARIEL
5th rowARIEL

Common Values

ValueCountFrequency (%)
NIVEA 2342628
13.8%
SHAMPOO3 1805328
 
10.6%
MAGGI 1740852
 
10.3%
DEOS1 1676376
 
9.9%
MUSCULO 1396980
 
8.2%
LIMPIEX 1053108
 
6.2%
LANCOME 644760
 
3.8%
SHAMPOO2 623268
 
3.7%
NATURA 601776
 
3.5%
SHAMPOO1 580284
 
3.4%
Other values (25) 4513320
26.6%

Length

2025-06-03T20:34:43.234632image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nivea 2342628
13.8%
shampoo3 1805328
 
10.6%
maggi 1740852
 
10.3%
deos1 1676376
 
9.9%
musculo 1396980
 
8.2%
limpiex 1053108
 
6.2%
lancome 644760
 
3.8%
shampoo2 623268
 
3.7%
natura 601776
 
3.5%
shampoo1 580284
 
3.4%
Other values (25) 4513320
26.6%

Most occurring characters

ValueCountFrequency (%)
O 12207456
 
11.4%
A 12164472
 
11.4%
M 8682768
 
8.1%
I 8532324
 
8.0%
E 8209944
 
7.7%
S 7629660
 
7.1%
N 4921668
 
4.6%
P 4835700
 
4.5%
L 4577796
 
4.3%
G 4190940
 
3.9%
Other values (25) 31163400
29.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 100195704
93.5%
Decimal Number 6060744
 
5.7%
Lowercase Letter 859680
 
0.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 12207456
12.2%
A 12164472
12.1%
M 8682768
 
8.7%
I 8532324
 
8.5%
E 8209944
 
8.2%
S 7629660
 
7.6%
N 4921668
 
4.9%
P 4835700
 
4.8%
L 4577796
 
4.6%
G 4190940
 
4.2%
Other values (15) 24242976
24.2%
Lowercase Letter
ValueCountFrequency (%)
o 214920
25.0%
m 107460
12.5%
p 107460
12.5%
r 107460
12.5%
t 107460
12.5%
a 107460
12.5%
d 107460
12.5%
Decimal Number
ValueCountFrequency (%)
1 2944404
48.6%
3 2127708
35.1%
2 988632
 
16.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 101055384
94.3%
Common 6060744
 
5.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 12207456
12.1%
A 12164472
12.0%
M 8682768
 
8.6%
I 8532324
 
8.4%
E 8209944
 
8.1%
S 7629660
 
7.5%
N 4921668
 
4.9%
P 4835700
 
4.8%
L 4577796
 
4.5%
G 4190940
 
4.1%
Other values (22) 25102656
24.8%
Common
ValueCountFrequency (%)
1 2944404
48.6%
3 2127708
35.1%
2 988632
 
16.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 107116128
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O 12207456
 
11.4%
A 12164472
 
11.4%
M 8682768
 
8.1%
I 8532324
 
8.0%
E 8209944
 
7.7%
S 7629660
 
7.1%
N 4921668
 
4.6%
P 4835700
 
4.5%
L 4577796
 
4.3%
G 4190940
 
3.9%
Other values (25) 31163400
29.1%

sku_size
Real number (ℝ)

High correlation 

Distinct67
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean459.64937
Minimum1
Maximum10000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size129.5 MiB
2025-06-03T20:34:43.496843image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q190
median220
Q3450
95-th percentile1000
Maximum10000
Range9999
Interquartile range (IQR)360

Descriptive statistics

Standard deviation895.1031
Coefficient of variation (CV)1.9473607
Kurtosis40.891263
Mean459.64937
Median Absolute Deviation (MAD)170
Skewness5.5270969
Sum7.8042395 × 109
Variance801209.55
MonotonicityNot monotonic
2025-06-03T20:34:43.740982image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200 1289520
 
7.6%
400 1203552
 
7.1%
50 1031616
 
6.1%
90 988632
 
5.8%
350 795204
 
4.7%
10 687744
 
4.1%
750 666252
 
3.9%
500 601776
 
3.5%
100 601776
 
3.5%
250 537300
 
3.2%
Other values (57) 8575308
50.5%
ValueCountFrequency (%)
1 171936
 
1.0%
2 214920
 
1.3%
3 21492
 
0.1%
4 171936
 
1.0%
5 322380
1.9%
6 85968
 
0.5%
8 128952
 
0.8%
10 687744
4.1%
12 171936
 
1.0%
15 150444
 
0.9%
ValueCountFrequency (%)
10000 42984
 
0.3%
5000 236412
1.4%
4000 21492
 
0.1%
3000 429840
2.5%
2000 42984
 
0.3%
1400 42984
 
0.3%
1250 21492
 
0.1%
1000 279396
1.6%
950 64476
 
0.4%
930 300888
1.8%

Interactions

2025-06-03T20:33:04.448520image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-03T20:30:40.723299image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-03T20:31:05.595892image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-03T20:31:33.548694image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-03T20:32:00.214113image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-03T20:32:20.975309image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-03T20:32:42.610200image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-03T20:33:08.709354image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-03T20:30:43.968219image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-03T20:31:09.172782image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-03T20:31:37.564844image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-03T20:32:03.185895image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-03T20:32:24.104871image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-03T20:32:45.723169image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-03T20:33:13.434419image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-03T20:30:47.464959image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-03T20:31:12.890653image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-03T20:31:42.136097image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-03T20:32:05.928056image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-03T20:32:27.166753image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-03T20:32:48.830132image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-03T20:33:17.911941image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-03T20:30:51.031841image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-03T20:31:16.619227image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-03T20:31:47.022321image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-03T20:32:08.556642image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-03T20:32:30.769530image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-03T20:32:51.819076image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-03T20:33:22.429025image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-03T20:30:54.758757image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-03T20:31:20.347061image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-03T20:31:50.626829image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-03T20:32:11.655270image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-03T20:32:33.491966image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-03T20:32:54.801616image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-03T20:33:26.977317image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-03T20:30:58.445947image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-03T20:31:23.981994image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-03T20:31:53.808495image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-03T20:32:14.670374image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-03T20:32:36.391354image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-03T20:32:57.787758image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-03T20:33:31.339594image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-03T20:31:02.066931image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-03T20:31:27.822259image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-03T20:31:56.980131image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-03T20:32:17.810188image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-03T20:32:39.501535image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-03T20:33:01.240400image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Correlations

2025-06-03T20:34:43.915799image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
brandcat1cat2cust_request_qtycust_request_tncustomer_idperiodoplan_precios_cuidadosproduct_idsku_sizestock_finaltn
brand1.0000.9990.8250.0080.0080.0000.0000.0810.3740.2720.0730.008
cat10.9991.0001.0000.0070.0080.0000.0000.0150.3120.2330.0640.007
cat20.8251.0001.0000.0070.0070.0000.0000.0510.2840.4540.0630.007
cust_request_qty0.0080.0070.0071.0000.996-0.3020.0050.021-0.1700.0070.0380.996
cust_request_tn0.0080.0080.0070.9961.000-0.3060.0010.001-0.1870.0210.0401.000
customer_id0.0000.0000.000-0.302-0.3061.0000.0000.0310.0000.000-0.000-0.306
periodo0.0000.0000.0000.0050.0010.0001.0000.0210.0000.0000.0500.001
plan_precios_cuidados0.0810.0150.0510.0210.0010.0310.0211.0000.0330.0070.0020.001
product_id0.3740.3120.284-0.170-0.1870.0000.0000.0331.000-0.518-0.120-0.187
sku_size0.2720.2330.4540.0070.0210.0000.0000.007-0.5181.0000.0670.021
stock_final0.0730.0640.0630.0380.040-0.0000.0500.002-0.1200.0671.0000.040
tn0.0080.0070.0070.9961.000-0.3060.0010.001-0.1870.0210.0401.000

Missing values

2025-06-03T20:33:40.138717image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
A simple visualization of nullity by column.
2025-06-03T20:33:59.942433image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

customer_idproduct_idperiodoplan_precios_cuidadoscust_request_qtycust_request_tntnstock_finalcat1cat2cat3brandsku_size
010001200012017-01-010.011.099.4386199.438610.0HCROPA LAVADOLiquidoARIEL3000
110001200012017-02-010.023.0198.84365198.843650.0HCROPA LAVADOLiquidoARIEL3000
210001200012017-03-010.033.092.4653792.465370.0HCROPA LAVADOLiquidoARIEL3000
310001200012017-04-010.08.013.2972813.297280.0HCROPA LAVADOLiquidoARIEL3000
410001200012017-05-010.015.0101.20711101.005630.0HCROPA LAVADOLiquidoARIEL3000
510001200012017-06-010.025.0128.04792128.047920.0HCROPA LAVADOLiquidoARIEL3000
610001200012017-07-010.018.0101.20711101.207110.0HCROPA LAVADOLiquidoARIEL3000
710001200012017-08-010.017.043.3393043.339300.0HCROPA LAVADOLiquidoARIEL3000
810001200012017-09-010.036.0289.35024289.350240.0HCROPA LAVADOLiquidoARIEL3000
910001200012017-10-010.022.0222.11389222.113890.0HCROPA LAVADOLiquidoARIEL3000
customer_idproduct_idperiodoplan_precios_cuidadoscust_request_qtycust_request_tntnstock_finalcat1cat2cat3brandsku_size
1697867010637212762019-03-010.00.00.00.01.68932PCPIEL1CaraNIVEA140
1697867110637212762019-04-010.00.00.00.01.57051PCPIEL1CaraNIVEA140
1697867210637212762019-05-010.00.00.00.01.30988PCPIEL1CaraNIVEA140
1697867310637212762019-06-010.00.00.00.01.29354PCPIEL1CaraNIVEA140
1697867410637212762019-07-010.00.00.00.01.28908PCPIEL1CaraNIVEA140
1697867510637212762019-08-010.00.00.00.01.08488PCPIEL1CaraNIVEA140
1697867610637212762019-09-010.00.00.00.00.87622PCPIEL1CaraNIVEA140
1697867710637212762019-10-010.00.00.00.01.05889PCPIEL1CaraNIVEA140
1697867810637212762019-11-010.00.00.00.01.06112PCPIEL1CaraNIVEA140
1697867910637212762019-12-010.00.00.00.01.05592PCPIEL1CaraNIVEA140

Duplicate rows

Most frequently occurring

customer_idproduct_idperiodoplan_precios_cuidadoscust_request_qtycust_request_tntnstock_finalcat1cat2cat3brandsku_size# duplicates
010001200102017-01-010.03.01.319141.319140.0HCROPA LAVADOPolvoLIMPIEX4002
110001200102017-02-010.01.00.113570.113570.0HCROPA LAVADOPolvoLIMPIEX4002
210001200102017-03-010.00.00.000000.000000.0HCROPA LAVADOPolvoLIMPIEX4002
310001200102017-04-010.00.00.000000.000000.0HCROPA LAVADOPolvoLIMPIEX4002
410001200102017-05-010.00.00.000000.000000.0HCROPA LAVADOPolvoLIMPIEX4002
510001200102017-06-010.00.00.000000.000000.0HCROPA LAVADOPolvoLIMPIEX4002
610001200102017-07-010.00.00.000000.000000.0HCROPA LAVADOPolvoLIMPIEX4002
710001200102017-08-010.00.00.000000.000000.0HCROPA LAVADOPolvoLIMPIEX4002
810001200102017-09-010.00.00.000000.000000.0HCROPA LAVADOPolvoLIMPIEX4002
910001200102017-10-010.00.00.000000.000000.0HCROPA LAVADOPolvoLIMPIEX4002